Design Philosophy for Optimizing Genetic Algorithms Through Embedded Intelligence

Traditionally Genetic algorithms are thought of as brute force approaches, aimed to arrive at solutions to problems which do not have a specific answer. In problems where the data is not structured for the general implementation of a specific idea, genetic algorithms are most useful. This paper proposes to mitigate the above problem of brute force approaches through elucidation of procedures ranging from exploratory analysis, followed by pattern analysis and classification. This novel conceptualization of the scheme and design will help in arriving at solutions through reduced iterations. Research conducted involves dropping of poorly performing hypotheses, controlled mutation, thereby adding a dimension of intelligence to evolutionary algorithms. The following paper describes the methodology used to solve the problem of addition of numbers using evolutionary algorithms of Neural Networks, whilst building intelligence into the system. The specific problem of addition has been dealt with in the following paper, however the same design philosophy can be extended for a paraphernalia of problems. The end goal is to obtain a generation of adroit and capable hypotheses to solve the problem in reduced number of iterations. The solution provided is generic and can be reused, it has been applied to a specific problem in the following paper

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks

We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We notice that: 1) information self structuring through sensory-motor coordination does not deterministically occur in Rn vector space, a generic multivariable space, but in SE(3), the group structure of the possible motions of a body in space; 2) it happens in a stochastic open ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework which aims to give new tools for the design of networks of new artificial self organizing, embodied and intelligent agents and the reverse engineering of natural ones. At this point, it represents much a theoretical conjecture and it has still to be experimentally verified whether this model will be useful in practice.

A Review on Optimal Operation of Distributed Network Embedded to Wind-Battery Storage System

Energy is a vital requirement for today's socio-economic welfare and development. But due to the continuous increase of the demand the conventional energy resources are depleting day by day and on the verge of extinction. Hence more renewable generation units are emphasised to integrate to the power network to supply the required demand. This incorporation of the distributed generation into the distributed network has the advantages of controllability, flexibility and tremendous potential if it can be exploited properly. However, due to their intermittent and unpredictable nature, there is a need for energy storages to ensure continuous operations, i.e., to meet the load all the time. There are many possible options for energy storage, but the most popular and technologically sound option is battery storage. Along with this battery storage system (BSS), a power conditioning system (PCS) has to be connected for generation of both active and reactive power from the BSS which in turn increases the overall installation cost of BSS. Moreover, the energy storage cost is a function of the storage device power, energy capacities and their specific costs depending on the chosen technology of optimization. Thus, profit from those renewable energy producers have to be maximized, and losses are to be minimized by using dynamic optimization techniques. But along with the advantages there comes the complexities due to the inclusion of distributed generation and the additional energy storages in the power system network. Moreover, it is highly critical to operate the vast system optimally. Hence there are lots of research had been done or are in process for finding the proper approach of optimization of the system. This paper presents a review of the current state of the optimization methods applied to renewable and sustainable energy source embedded with the Energy storage for maximization of the revenue obtained from the power trading to the network

Intelligent MANET optimisation system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%

A Review on Optimal Operation of Distributed Network Embedded to Wind-Battery Storage System

Energy is a vital requirement for today’s socio-economic welfare and development. But due to the continuous increase of the demand the conventional energy resources are depleting day by day and on the verge of extinction. Hence more renewable generation units are emphasised to integrate to the power network to supply the required demand. This incorporation of the distributed generation into the distributed network has the advantages of controllability, flexibility and tremendous potential if it can be exploited properly. However, due to their intermittent and unpredictable nature, there is a need for energy storages to ensure continuous operations, i.e., to meet the load all the time. There are many possible options for energy storage, but the most popular and technologically sound option is battery storage. Along with this battery storage system (BSS), a power conditioning system (PCS) has to be connected for generation of both active and reactive power from the BSS which in turn increases the overall installation cost of BSS. Moreover, the energy storage cost is a function of the storage device power, energy capacities and their specific costs depending on the chosen technology of optimization. Thus, profit from those renewable energy producers have to be maximized, and losses are to be minimized by using dynamic optimization techniques. But along with the advantages there comes the complexities due to the inclusion of distributed generation and the additional energy storages in the power system network. Moreover, it is highly critical to operate the vast system optimally. Hence there are lots of research had been done or are in process for finding the proper approach of optimization of the system. This paper presents a review of the current state of the optimization methods applied to renewable and sustainable energy source embedded with the Energy storage for maximization of the revenue obtained from the power trading to the network

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems